Carbon dioxide (CO₂), whose atmospheric volume has been increasing, is the principal greenhouse gas (GHG) that causes global warming and climate change. Climate change from increasing greenhouse gases (GHGs) has broad health and environmental influences. Human-emitted GHGs constitute a significant cause of global warming. The main objective of this article is to assess the influence of technological innovation, economic growth, fossil energy use, renewable energy consumption, and globalisation on CO₂ emissions (CO₂Es) in South Africa over the observation period 1990–2020. The Dynamic Ordinary Least Squares (DOLS) estimator was implemented to examine the relationship between the constituents. The study results indicate that economic growth, fossil energy use, technological innovation, and globalisation boost CO₂Es, whereas renewable energy consumption reduces CO₂Es. The output of the canonical cointegrating regression and fully modified least squares estimators also supports the output of the DOLS estimator. We concluded that, in South Africa, a rise in economic growth, fossil energy use, technological innovation, and globalisation results in an upsurge in CO₂Es. In contrast, an increase in renewable energy consumption leads to a reduction in CO₂Es. This research work contributes to the discourse on CO₂Es in the South African context. The study recommends that policymakers in South Africa should design policies that foster the formation of a low-carbon country, the consumption of renewable energy, and the implementation of environment-friendly technological innovations that reduce the use of fossil energy. Graphical Abstract

Debate on the validity of the Law of Proportionate Effect (LPE) on firm growth is ongoing decades after it was postulated by Gibrat in 1931. The theoretical model which asserts that firm growth follows a random walk has been largely tested in developed economies using data from non-incubated firms, with scanty research in developing regions like Africa. This paper, therefore, aims to address this gap by being the first to assess the validity of Gibrat's law on incubated small, medium, and micro enterprises (SMMEs) in South Africa. The study utilised four-year panel data from 300 incubated SMMEs across the country, for the period between 2018 to 2021. Utilising the Law's generalised growth rate model, the generalised least square regression modelling was harnessed, using R Software. The findings, using sales as firm size proxy, confirmed Gibrat’s Law. The results showed that firm size had no effect on the sales growth rate of incubated firms, on the other hand when employment proxied performance the LPE was rejected. The findings provide important implications for both practitioners and pertinent stakeholders in the SMME sector in South Africa.

Purpose: The COVID-19 pandemic has created unprecedented challenges for businesses worldwide, especially for Small and medium-sized enterprises (SMEs). The pandemic has created new risks and uncertainties that SMEs must navigate to remain operational and competitive. To address these challenges, SMEs need to adopt innovative practices to survive and thrive. Recent studies have shown that data analytics is increasingly becoming a key factor in driving firm performance. Thus, this study aims to empirically assess the importance of data analytics in driving resilient performance. Essentially this paper elucidates the strategic role of data analytics as one of the key components of an artificial intelligence driven world, to drive sustainable firm performance. Methodology: The research employed a distinctive dataset of 450 SMEs in South Africa. Machine learning techniques, particularly Random Forest and Support Vector Regression (SVR), were utilised to model the influence of data analytics on SME performance during the Covid-19 pandemic. This approach facilitated a detailed examination of the correlation between data analytics adoption and organisational resilience during unprecedented circumstances. Results: Data analytics can help SMEs prioritize urgent matters, ultimately improving their performance. Thus, the study recommends analytics software. With the help of analytics software, SMEs can gain valuable insights into critical issues that require immediate attention. By embracing these data analytics solutions, SMEs can effectively leverage their data to generate valuable insights that support decision-making processes. Originality/Relevance: In the context of a developing country during the COVID-19 pandemic, this study addresses a substantial gaps in the literature by concentrating on the role of data analytics in the performance of SMEs. Although prior research has illustrated the significance of data analytics for SMEs in developed countries, this study offers new perspectives on its implementation and influence in South Africa. The use of advanced machine learning techniques to analyze a substantial dataset of SMEs adds methodological rigor to the research. Keywords: SMEs; Data Analytics; Risk; Performance